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Molecular chess? Hallmarks of anti-cancer drug resistance

Overview of attention for article published in BMC Cancer, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
9 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
127 Dimensions

Readers on

mendeley
206 Mendeley
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Title
Molecular chess? Hallmarks of anti-cancer drug resistance
Published in
BMC Cancer, January 2017
DOI 10.1186/s12885-016-2999-1
Pubmed ID
Authors

Ian A. Cree, Peter Charlton

Abstract

The development of resistance is a problem shared by both classical chemotherapy and targeted therapy. Patients may respond well at first, but relapse is inevitable for many cancer patients, despite many improvements in drugs and their use over the last 40 years. Resistance to anti-cancer drugs can be acquired by several mechanisms within neoplastic cells, defined as (1) alteration of drug targets, (2) expression of drug pumps, (3) expression of detoxification mechanisms, (4) reduced susceptibility to apoptosis, (5) increased ability to repair DNA damage, and (6) altered proliferation. It is clear, however, that changes in stroma and tumour microenvironment, and local immunity can also contribute to the development of resistance. Cancer cells can and do use several of these mechanisms at one time, and there is considerable heterogeneity between tumours, necessitating an individualised approach to cancer treatment. As tumours are heterogeneous, positive selection of a drug-resistant population could help drive resistance, although acquired resistance cannot simply be viewed as overgrowth of a resistant cancer cell population. The development of such resistance mechanisms can be predicted from pre-existing genomic and proteomic profiles, and there are increasingly sophisticated methods to measure and then tackle these mechanisms in patients. The oncologist is now required to be at least one step ahead of the cancer, a process that can be likened to 'molecular chess'. Thus, as well as an increasing role for predictive biomarkers to clinically stratify patients, it is becoming clear that personalised strategies are required to obtain best results.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 206 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
Unknown 205 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 25%
Researcher 30 15%
Student > Master 29 14%
Student > Bachelor 23 11%
Student > Doctoral Student 11 5%
Other 26 13%
Unknown 35 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 68 33%
Agricultural and Biological Sciences 32 16%
Medicine and Dentistry 22 11%
Pharmacology, Toxicology and Pharmaceutical Science 11 5%
Chemistry 6 3%
Other 25 12%
Unknown 42 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2017.
All research outputs
#2,978,050
of 15,681,050 outputs
Outputs from BMC Cancer
#748
of 5,850 outputs
Outputs of similar age
#91,653
of 386,016 outputs
Outputs of similar age from BMC Cancer
#69
of 654 outputs
Altmetric has tracked 15,681,050 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,850 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 386,016 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 654 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.